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Glama

Social Fetch

by io.github.social-freak-ltd

Get Facebook Ad Library ad

facebook_adLibrary_ad_get
Read-only

Get a single Facebook Ad Library ad by archive id or public Ad Library URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoPublic Facebook Ad Library URL for the ad.
adIdNoFacebook Ad Library archive id for the ad.
trimNoWhen true, requests a smaller payload before normalization.
contextYesDescribe the user's underlying goal in one sentence — not the tool you are calling.
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
conversation_idNoEcho the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.
includeTranscriptNoWhen true, includes a plain-text transcript when available for the ad video.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Describe the user's underlying goal in one sentence — not the tool you are calling.",
+  "type": "string"
+}
    • addedInput schema / properties / conversation_id
      Added value: +{
+  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
+  "type": "string"
+}
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}
    • addedInput schema / required
      Added value: +[
+  "context",
+  "llm_model"
+]
  2. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  3. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  4. Changed2 schema fields changed
    • changedInput schema / properties / includeTranscript / anyOf
      Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "boolean"
-  }
-]New value: +[
+  {
+    "type": "boolean"
+  },
+  {
+    "enum": [
+      "0",
+      "1",
+      "true",
+      "false"
+    ],
+    "type": "string"
+  }
+]
    • changedInput schema / properties / trim / anyOf
      Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "boolean"
-  }
-]New value: +[
+  {
+    "type": "boolean"
+  },
+  {
+    "enum": [
+      "0",
+      "1",
+      "true",
+      "false"
+    ],
+    "type": "string"
+  }
+]
  5. First observed

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint and openWorldHint, covering the core safety and data-freshness profile. The description adds the single-ad and dual-locator behavior, but it does not disclose return-value shape, error behavior for invalid ids, or how trim/transcript affect the response. This is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with no wasted words, front-loading the verb, resource, and input forms. It is compact while conveying everything the agent needs at the description level.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the fully documented 7-parameter schema, readOnly/openWorld annotations, and the simple getter pattern, the description covers the essential selection semantics. It could mention return structure or expected outputs since no output schema exists, but the tool name and sibling conventions make the result sufficiently predictable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the description correctly avoids restating each parameter. It does add useful relational meaning by indicating that either adId or url may be used as the locator, which is not enforced in the schema's required list (only context and llm_model are required).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Get'), names a precise resource ('single Facebook Ad Library ad'), and states the two accepted locator forms ('archive id or public Ad Library URL'). This clearly distinguishes it from facebook_adLibrary_ads_search_get and from platform-specific siblings like google_adLibrary_ad_get and linkedin_adLibrary_ad_get.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly implies the intended use: when the agent already has a specific ad identifier or public URL and wants one ad, this is the tool. It does not explicitly name the search sibling as the alternative for discovery workflows, so it falls just short of full exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.4/5.0
Disambiguation5/5

Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.

Naming Consistency5/5

All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).

Tool Count2/5

The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.

Completeness4/5

The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.

Resources